Mailman 3 October 2012 - SciPy-User

Return sigmas from curve_fit
by Gökhan Sever Oct. 17, 2012

Oct. 17, 2012

Hello, Is there a way to return standard deviations of the best fit parameters from curve_fit like in IDL's curvefit function? PS: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fi… http://www.exelisvis.com/docs/CURVEFIT.html -- Gökhan

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Fw: Memory required for sparse calculation
by Dinesh B Vadhia Oct. 17, 2012

Oct. 17, 2012

Sorry, ignore question. Best ... From: Dinesh B Vadhia Sent: Tuesday, October 16, 2012 1:17 AM To: scipy-user(a)scipy.org Subject: Memory required for sparse calculation Nathan Bell answered this question a long time ago but I cannot find the archive reference to it: in the Scipy matrix calculation y <- Ax, what is the memory required for the calculation to take place in addition to A (ignore memory requirements for y and x)?

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a TOST
by josef.pktd＠gmail.com Oct. 17, 2012

Oct. 17, 2012

https://gist.github.com/3900314 label: statistical tests and options that are missing in scipy and statsmodels. Josef

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Masked array output from scipy.io.netcdf_file
by Gökhan Sever Oct. 17, 2012

Oct. 17, 2012

Hello, Is there any interest out to read data as masked array if a netcdf file variable contains appropriate filled_value attribute or if asked explicitly using scipy.io.netcdf_file? Right now, I use netcdf4-python's Dataset constructor to read bunch of variables, and I was planning to replace it with netcdf_file module. However, I see that netcdf_file cannot automatically construct masked arrays from the file. I only read from netcdf files, not writing, so this would eliminate an external dependency to run my script. Thanks. -- Gökhan

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SmoothBivariateSpline giving wrong result
by Kevin Gullikson Oct. 16, 2012

Oct. 16, 2012

Hi all, I am trying to do a 2d interpolation of unstructured data. To try to get a feel for how the functions work, I tried a simple test case and it is not working. See below: In [24]: x = numpy.arange(10.) In [25]: y = numpy.arange(10.) In [26]: z = x**2 + y**2 In [27]: fcn = SmoothBivariateSpline(x,y,z, s=0, kx=1, ky=1) In [28]: fcn(1,1) Out[28]: array([[ 0.]]) In [29]: fcn(1,5) Out[29]: array([[ 0.]]) What am I doing wrong? It seems like z should be a 2d array or something, but the documentation explicitly says it is a 1d array. Much thanks, and I look forward to feeling stupid! Kevin Gullikson

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Memory required for sparse calculation
by Dinesh B Vadhia Oct. 16, 2012

Oct. 16, 2012

Nathan Bell answered this question a long time ago but I cannot find the archive reference to it: in the Scipy matrix calculation y <- Ax, what is the memory required for the calculation to take place in addition to A (ignore memory requirements for y and x)?

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distributions: negative scale
by josef.pktd＠gmail.com Oct. 16, 2012

Oct. 16, 2012

<sending a draft from last week for general amusement> https://github.com/scipy/scipy/pull/336 I'm trying to figure out what's the difference between gamma and Pearson Type III distribution. In my quick reading there is no difference in the general form. However, it looks like we should allow for a negative scale. Why doesn't scipy.stats.distributions allow for a negative scale? scaling is just a linear transformation, and it's the same for the pdf whether the scale factor is positive or negative. oops, for the cdf the integration limits are reversed, .... ok, that requires work, so we better postpone this indefinitely. Josef

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[ANN] CFP: SciPy India 2012 -- Dec 27-29 -- IIT Bombay
by Jarrod Millman Oct. 15, 2012

Oct. 15, 2012

Hello, The CFP for SciPy India 2012, to be held in IIT Bombay from December 27-29 is open. Please spread the word! Scipy.in is a conference providing opportunities to spread the use of the Python programming language in the Scientific Computing community in India. It provides a unique opportunity to interact with the "Who's who" of the Python for Scientific Computing fraternity and learn, understand, participate, and contribute to Scientific Computing using Python. Attendees of the conference and participants of the sprints planned will be able to access and review the tools available. They will also be able to learn domain-specific applications and how the tools apply to a plethora of application problems. One of the goals of the conference is to combine education, engineering, and science with computing through the medium of Python. This conference also aims to spread the use of Python for Scientific Computing in various fields and among different communities. Call for Papers ================ We look forward to your submissions on the use of Python for Scientific Computing and Education. This includes pedagogy, exploration, modeling and analysis from both applied and developmental perspectives. We welcome contributions from academia as well as industry. Submission of Papers ===================== If you wish to present your paper using this platform, please submit an abstract of 300 to 700 words describing the topic, including its relevance to scientific computing. Based on the number and quality of the submissions, the conference organizers will allot 10 - 30 minutes for each accepted talk. In addition to these talks, there will be an open session of lightning talks, during which any attendee who wishes to talk on a pertinent topic is invited to do a presentation not exceeding five minutes in duration. If you wish to present a talk at the conference, please follow the guidelines below. Submission Guidelines ====================== - Submit your proposals at scipy(a)fossee.in - Submissions whose main purpose is to promote a commercial product or service will be refused. - All accepted proposals must be presented at the SciPy conference by at least one author. Important Dates ================ - Call for proposals start: 27th September 2012, Thursday - Call for proposals end: 1st November 2012, Thursday - List of accepted proposals will be published: 19th November 2012, Monday - Submission of first presentation: 10th December 2012, Monday - Submission of final presentation(with final changes): 20th December 2012, Thursday

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empirical CDF
by Degang Wu Oct. 14, 2012

Oct. 14, 2012

Hi, Is Scipy able to calculate empirical CDF (calculating a CDF from a sequence of random samples)? I have searched the documentation for quite a while, but have found nothing useful.

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Volume of convex hulls from Delaunay triangulation
by Eric Bruning Oct. 14, 2012

Oct. 14, 2012

Are there any known edge or degenerate cases with the simplex volume calculation in scipy.spatial's test_qhull.py (1)? Applying this method to my dataset, some of the volumes are negative and some are positive, which might just be the 3D analogue of area with a different surface normal. The goal is to get the total volume of the convex hull, which I assume I can just do by summing the absolute values of the individual simplex volumes. (1) https://github.com/scipy/scipy/blob/master/scipy/spatial/tests/test_qhull.p… Thanks, Eric

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